Generalization to different types of anomalies. We propose reporting the performance according to the anomaly type encountered. This analysis can help identify the most suitable algorithm for a given application. To that aim, the TODS benchmark, which encloses the annotation of 5 different types of outliers depicted in Section 4.1 is used.
Although the definition of different anomaly types has been reported in several references, very few works have carried out an experimental study with respect to the anomaly type. A rare example we can mention is the work of [1]}. Nevertheless, it can be noted that in this paper, recent DL-based state-of-the-art approaches such as GDN [2]}, USAD [3]}, and MTAD-GAT [4]} are not evaluated. For each anomaly type, the percentage of well-detected anomalies is reported.
